Conclusion Based on Four Tables

Table 1: Raw Data with Multicollinearity

• The SVR model performed the best with the highest R2 scores and lowest MSE values for both train and test sets.
• The DecisionTreeReg model performed the worst with the lowest R2 scores and highest MSE values for both train and test sets.
• The other models had similar performances with R2 scores ranging from 0.926 to 0.969 and MSE values ranging from 1.403 to 3.402 for the test set.
• The presence of multicollinearity in the data might have affected the performances of the models.

Table 2: Engineered Features

• The RandomForestReg model performed the best with the highest R2 scores and lowest MSE values for both train and test sets.
• The DecisionTreeReg model performed the worst with the lowest R2 score for the test set.
• The other models had moderate performances with R2 scores ranging from 0.761 to 0.946 and MSE values ranging from 2.405 to 11.086 for the test set.
• The performances of the models improved with the introduction of engineered features compared to the raw data with multicollinearity.

Table 3: Raw Data and Engineered Features

• The RandomForestReg model performed the best with the highest R2 scores and lowest MSE values for both train and test sets.
• The DecisionTreeReg model performed the worst with the lowest R2 score for the test set.
• The other models had moderate performances with R2 scores ranging from 0.892 to 0.955 and MSE values ranging from 2.465 to 5.349 for the test set.
• The performance of the models improved with the addition of raw data features, but not as much as when engineered features were introduced.

Table 4: Engineered Features without Rain and Wind Speed

• The RandomForestReg model performed the best with the highest R2 scores and lowest MSE values for both train and test sets.
• The DecisionTreeReg model performed the worst with the lowest R2 score for the test set.
• The other models had moderate performances with R2 scores ranging from 0.760 to 0.946 and MSE values ranging from 2.278 to 11.139 for the test set.
• The performance of the models were similar to that of Table 2, where all engineered features were included.

Overall Conclusion

• The RandomForestReg model performed the best in all four tables, indicating that it is the most suitable model for this dataset.
• The performances of the other models were relatively similar in all tables, with some variations depending on the type of data and features used.
• The presence of multicollinearity in the raw data affected the performances of some models.
• The introduction of engineered features improved the performances of the models significantly.
• Removing certain features, such as rain and wind speed, did not significantly affect the performance of the models.